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<article id="content">
<header>
<h1 class="title">Module <code>silk.backbones.silk.silk</code></h1>
</header>
<section id="section-intro">
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python"># Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.

# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.

from functools import partial
from typing import Iterable, Tuple, Union

import torch
import torch.nn as nn
from silk.backbones.abstract.shared_backbone_multiple_heads import (
    SharedBackboneMultipleHeads,
)
from silk.backbones.loftr.resnet_fpn import ResNetFPN_8_2
from silk.backbones.superpoint.magicpoint import (
    Backbone as VGGBackbone,
    DetectorHead as VGGDetectorHead,
    MagicPoint,
)
from silk.backbones.superpoint.superpoint import (
    DescriptorHead as VGGDescriptorHead,
    SuperPoint,
)
from silk.flow import AutoForward
from silk.flow import Flow
from silk.models.superpoint_utils import get_dense_positions


def from_feature_coords_to_image_coords(model, desc_positions):
    if isinstance(desc_positions, tuple):
        return tuple(
            from_feature_coords_to_image_coords(
                model,
                dp,
            )
            for dp in desc_positions
        )
    coord_mapping = model.coordinate_mapping_composer.get(&#34;images&#34;, &#34;raw_descriptors&#34;)
    desc_positions = torch.cat(
        [
            coord_mapping.reverse(desc_positions[..., :2]),
            desc_positions[..., 2:],
        ],
        dim=-1,
    )

    return desc_positions


class SiLKBase(AutoForward, torch.nn.Module):
    def __init__(
        self,
        backbone,
        input_name: str = &#34;images&#34;,
        backbone_output_name: Union[str, Tuple[str]] = &#34;features&#34;,
        default_outputs: Union[str, Iterable[str]] = (&#34;descriptors&#34;, &#34;score&#34;),
    ):
        torch.nn.Module.__init__(self)

        self.backbone = SharedBackboneMultipleHeads(
            backbone=backbone,
            input_name=input_name,
            backbone_output_name=backbone_output_name,
        )

        self.detector_heads = set()
        self.descriptor_heads = set()

        AutoForward.__init__(self, self.backbone.flow, default_outputs=default_outputs)

    @property
    def coordinate_mapping_composer(self):
        return self.backbone.coordinate_mapping_composer

    def add_detector_head(self, head_name, head, backbone_output_name=None):
        self.backbone.add_head_to_backbone_output(head_name, head, backbone_output_name)
        self.detector_heads.add(head_name)

    def add_descriptor_head(self, head_name, head, backbone_output_name=None):
        self.backbone.add_head_to_backbone_output(head_name, head, backbone_output_name)
        self.descriptor_heads.add(head_name)


class SiLKVGG(SiLKBase):
    def __init__(
        self,
        in_channels,
        *,
        feat_channels: int = 128,
        lat_channels: int = 128,
        desc_channels: int = 128,
        use_batchnorm: bool = True,
        backbone=None,
        detector_head=None,
        descriptor_head=None,
        detection_threshold: float = 0.8,
        detection_top_k: int = 100,
        nms_dist=4,
        border_dist=4,
        descriptor_scale_factor: float = 1.0,
        learnable_descriptor_scale_factor: bool = False,
        normalize_descriptors: bool = True,
        padding: int = 1,
        **base_kwargs,
    ) -&gt; None:

        backbone = (
            VGGBackbone(
                num_channels=in_channels,
                use_batchnorm=use_batchnorm,
                use_max_pooling=False,
                padding=padding,
            )
            if backbone is None
            else backbone
        )

        detector_head = (
            VGGDetectorHead(
                in_channels=feat_channels,
                lat_channels=lat_channels,
                out_channels=1,
                use_batchnorm=use_batchnorm,
                padding=padding,
            )
            if detector_head is None
            else detector_head
        )

        descriptor_head = (
            VGGDescriptorHead(
                in_channels=feat_channels,
                out_channels=desc_channels,
                use_batchnorm=use_batchnorm,
                padding=padding,
            )
            if descriptor_head is None
            else descriptor_head
        )

        SiLKBase.__init__(
            self,
            backbone=backbone,
            **base_kwargs,
        )

        self.add_detector_head(&#34;logits&#34;, detector_head)
        self.add_descriptor_head(&#34;raw_descriptors&#34;, descriptor_head)

        self.descriptor_scale_factor = nn.parameter.Parameter(
            torch.tensor(descriptor_scale_factor),
            requires_grad=learnable_descriptor_scale_factor,
        )
        self.normalize_descriptors = normalize_descriptors

        MagicPoint.add_detector_head_post_processing(
            self.flow,
            &#34;logits&#34;,
            prefix=&#34;&#34;,
            cell_size=1,
            detection_threshold=detection_threshold,
            detection_top_k=detection_top_k,
            nms_dist=nms_dist,
            border_dist=border_dist,
        )

        SiLKVGG.add_descriptor_head_post_processing(
            self.flow,
            input_name=self.backbone.input_name,
            descriptor_head_output_name=&#34;raw_descriptors&#34;,
            prefix=&#34;&#34;,
            scale_factor=self.descriptor_scale_factor,
            normalize_descriptors=normalize_descriptors,
        )

    @staticmethod
    def add_descriptor_head_post_processing(
        flow: Flow,
        input_name: str = &#34;images&#34;,
        descriptor_head_output_name: str = &#34;raw_descriptors&#34;,
        positions_name: str = &#34;positions&#34;,
        prefix: str = &#34;superpoint.&#34;,
        scale_factor: float = 1.0,
        normalize_descriptors: bool = True,
    ):
        flow.define_transition(
            f&#34;{prefix}normalized_descriptors&#34;,
            partial(
                SuperPoint.normalize_descriptors,
                scale_factor=scale_factor,
                normalize=normalize_descriptors,
            ),
            descriptor_head_output_name,
        )
        flow.define_transition(
            f&#34;{prefix}dense_descriptors&#34;,
            SiLKVGG.get_dense_descriptors,
            f&#34;{prefix}normalized_descriptors&#34;,
        )
        flow.define_transition(f&#34;{prefix}image_size&#34;, SuperPoint.image_size, input_name)
        flow.define_transition(
            f&#34;{prefix}sparse_descriptors&#34;,
            partial(
                SiLKVGG.sparsify_descriptors,
                scale_factor=scale_factor,
                normalize_descriptors=normalize_descriptors,
            ),
            descriptor_head_output_name,
            positions_name,
        )
        flow.define_transition(
            f&#34;{prefix}sparse_positions&#34;,
            lambda x: x,
            positions_name,
        )
        flow.define_transition(
            f&#34;{prefix}dense_positions&#34;,
            SiLKVGG.get_dense_positions,
            &#34;probability&#34;,
        )

    @staticmethod
    def get_dense_positions(probability):
        batch_size = probability.shape[0]
        device = probability.device
        dense_positions = get_dense_positions(
            probability.shape[2],
            probability.shape[3],
            device,
            batch_size=batch_size,
        )

        dense_probability = probability.reshape(probability.shape[0], -1, 1)
        dense_positions = torch.cat((dense_positions, dense_probability), dim=2)

        return dense_positions

    @staticmethod
    def get_dense_descriptors(normalized_descriptors):
        dense_descriptors = normalized_descriptors.reshape(
            normalized_descriptors.shape[0],
            normalized_descriptors.shape[1],
            -1,
        )
        dense_descriptors = dense_descriptors.permute(0, 2, 1)
        return dense_descriptors

    @staticmethod
    def sparsify_descriptors(
        raw_descriptors,
        positions,
        scale_factor: float = 1.0,
        normalize_descriptors: bool = True,
    ):
        sparse_descriptors = []
        for i, pos in enumerate(positions):
            pos = pos[:, :2]
            pos = pos.floor().long()

            descriptors = raw_descriptors[i, :, pos[:, 0], pos[:, 1]].T

            # L2 normalize the descriptors
            descriptors = SuperPoint.normalize_descriptors(
                descriptors,
                scale_factor,
                normalize_descriptors,
            )

            sparse_descriptors.append(descriptors)
        return tuple(sparse_descriptors)


class SiLKLoFTR(SiLKBase):
    def __init__(
        self,
        in_channels,
        *,
        initial_dim: int = 128,
        block_dims: Tuple[int] = (128, 196, 256),
        lat_channels: int = 256,
        desc_channels: int = 256,
        use_batchnorm: bool = True,
        backbone=None,
        detector_head=None,
        descriptor_head=None,
        detection_threshold: float = 0.8,
        detection_top_k: int = 100,
        nms_dist=4,
        border_dist=4,
        descriptor_scale_factor: float = 1.0,
        learnable_descriptor_scale_factor: bool = False,
        resolution_preserving: bool = False,
        padding: int = 1,
        **base_kwargs,
    ) -&gt; None:

        backbone = (
            ResNetFPN_8_2(
                {
                    &#34;in_channels&#34;: in_channels,
                    &#34;initial_dim&#34;: initial_dim,
                    &#34;block_dims&#34;: block_dims,
                    &#34;resolution_preserving&#34;: resolution_preserving,
                    &#34;padding&#34;: padding,
                }
            )
            if backbone is None
            else backbone
        )

        feat_channels = block_dims[0]

        detector_head = (
            VGGDetectorHead(
                in_channels=feat_channels,
                lat_channels=lat_channels,
                out_channels=1,
                use_batchnorm=use_batchnorm,
                padding=padding,
            )
            if detector_head is None
            else detector_head
        )

        descriptor_head = (
            VGGDescriptorHead(
                in_channels=feat_channels,
                out_channels=desc_channels,
                use_batchnorm=use_batchnorm,
                padding=padding,
            )
            if descriptor_head is None
            else descriptor_head
        )

        super().__init__(
            backbone=backbone,
            backbone_output_name=(&#34;low_res_features&#34;, &#34;features&#34;),
            **base_kwargs,
        )

        self.add_detector_head(&#34;logits&#34;, detector_head, backbone_output_name=&#34;features&#34;)
        self.add_descriptor_head(
            &#34;raw_descriptors&#34;,
            descriptor_head,
            backbone_output_name=&#34;features&#34;,
        )

        # TODO : Learneable ?
        if learnable_descriptor_scale_factor:
            self.descriptor_scale_factor = nn.parameter.Parameter(
                torch.tensor(descriptor_scale_factor)
            )
        else:
            self.descriptor_scale_factor = descriptor_scale_factor

        MagicPoint.add_detector_head_post_processing(
            self.flow,
            &#34;logits&#34;,
            prefix=&#34;&#34;,
            cell_size=1,
            detection_threshold=detection_threshold,
            detection_top_k=detection_top_k,
            nms_dist=nms_dist,
            border_dist=border_dist,
        )

        SiLKVGG.add_descriptor_head_post_processing(
            self.flow,
            input_name=self.backbone.input_name,
            descriptor_head_output_name=&#34;raw_descriptors&#34;,
            prefix=&#34;&#34;,
            scale_factor=self.descriptor_scale_factor,
        )</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-functions">Functions</h2>
<dl>
<dt id="silk.backbones.silk.silk.from_feature_coords_to_image_coords"><code class="name flex">
<span>def <span class="ident">from_feature_coords_to_image_coords</span></span>(<span>model, desc_positions)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def from_feature_coords_to_image_coords(model, desc_positions):
    if isinstance(desc_positions, tuple):
        return tuple(
            from_feature_coords_to_image_coords(
                model,
                dp,
            )
            for dp in desc_positions
        )
    coord_mapping = model.coordinate_mapping_composer.get(&#34;images&#34;, &#34;raw_descriptors&#34;)
    desc_positions = torch.cat(
        [
            coord_mapping.reverse(desc_positions[..., :2]),
            desc_positions[..., 2:],
        ],
        dim=-1,
    )

    return desc_positions</code></pre>
</details>
</dd>
</dl>
</section>
<section>
<h2 class="section-title" id="header-classes">Classes</h2>
<dl>
<dt id="silk.backbones.silk.silk.SiLKBase"><code class="flex name class">
<span>class <span class="ident">SiLKBase</span></span>
<span>(</span><span>backbone, input_name: str = 'images', backbone_output_name: Union[str, Tuple[str]] = 'features', default_outputs: Union[str, Iterable[str]] = ('descriptors', 'score'))</span>
</code></dt>
<dd>
<div class="desc"><p>Base class for all neural network modules.</p>
<p>Your models should also subclass this class.</p>
<p>Modules can also contain other Modules, allowing to nest them in
a tree structure. You can assign the submodules as regular attributes::</p>
<pre><code>import torch.nn as nn
import torch.nn.functional as F

class Model(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))
</code></pre>
<p>Submodules assigned in this way will be registered, and will have their
parameters converted too when you call :meth:<code>to</code>, etc.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>As per the example above, an <code>__init__()</code> call to the parent class
must be made before assignment on the child.</p>
</div>
<p>:ivar training: Boolean represents whether this module is in training or
evaluation mode.
:vartype training: bool</p>
<p>Initializes internal Module state, shared by both nn.Module and ScriptModule.</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class SiLKBase(AutoForward, torch.nn.Module):
    def __init__(
        self,
        backbone,
        input_name: str = &#34;images&#34;,
        backbone_output_name: Union[str, Tuple[str]] = &#34;features&#34;,
        default_outputs: Union[str, Iterable[str]] = (&#34;descriptors&#34;, &#34;score&#34;),
    ):
        torch.nn.Module.__init__(self)

        self.backbone = SharedBackboneMultipleHeads(
            backbone=backbone,
            input_name=input_name,
            backbone_output_name=backbone_output_name,
        )

        self.detector_heads = set()
        self.descriptor_heads = set()

        AutoForward.__init__(self, self.backbone.flow, default_outputs=default_outputs)

    @property
    def coordinate_mapping_composer(self):
        return self.backbone.coordinate_mapping_composer

    def add_detector_head(self, head_name, head, backbone_output_name=None):
        self.backbone.add_head_to_backbone_output(head_name, head, backbone_output_name)
        self.detector_heads.add(head_name)

    def add_descriptor_head(self, head_name, head, backbone_output_name=None):
        self.backbone.add_head_to_backbone_output(head_name, head, backbone_output_name)
        self.descriptor_heads.add(head_name)</code></pre>
</details>
<h3>Ancestors</h3>
<ul class="hlist">
<li><a title="silk.flow.AutoForward" href="../../flow.html#silk.flow.AutoForward">AutoForward</a></li>
<li>torch.nn.modules.module.Module</li>
</ul>
<h3>Subclasses</h3>
<ul class="hlist">
<li><a title="silk.backbones.silk.silk.SiLKLoFTR" href="#silk.backbones.silk.silk.SiLKLoFTR">SiLKLoFTR</a></li>
<li><a title="silk.backbones.silk.silk.SiLKVGG" href="#silk.backbones.silk.silk.SiLKVGG">SiLKVGG</a></li>
</ul>
<h3>Class variables</h3>
<dl>
<dt id="silk.backbones.silk.silk.SiLKBase.dump_patches"><code class="name">var <span class="ident">dump_patches</span> : bool</code></dt>
<dd>
<div class="desc"></div>
</dd>
<dt id="silk.backbones.silk.silk.SiLKBase.training"><code class="name">var <span class="ident">training</span> : bool</code></dt>
<dd>
<div class="desc"></div>
</dd>
</dl>
<h3>Instance variables</h3>
<dl>
<dt id="silk.backbones.silk.silk.SiLKBase.coordinate_mapping_composer"><code class="name">var <span class="ident">coordinate_mapping_composer</span></code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">@property
def coordinate_mapping_composer(self):
    return self.backbone.coordinate_mapping_composer</code></pre>
</details>
</dd>
</dl>
<h3>Methods</h3>
<dl>
<dt id="silk.backbones.silk.silk.SiLKBase.add_descriptor_head"><code class="name flex">
<span>def <span class="ident">add_descriptor_head</span></span>(<span>self, head_name, head, backbone_output_name=None)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def add_descriptor_head(self, head_name, head, backbone_output_name=None):
    self.backbone.add_head_to_backbone_output(head_name, head, backbone_output_name)
    self.descriptor_heads.add(head_name)</code></pre>
</details>
</dd>
<dt id="silk.backbones.silk.silk.SiLKBase.add_detector_head"><code class="name flex">
<span>def <span class="ident">add_detector_head</span></span>(<span>self, head_name, head, backbone_output_name=None)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def add_detector_head(self, head_name, head, backbone_output_name=None):
    self.backbone.add_head_to_backbone_output(head_name, head, backbone_output_name)
    self.detector_heads.add(head_name)</code></pre>
</details>
</dd>
<dt id="silk.backbones.silk.silk.SiLKBase.forward"><code class="name flex">
<span>def <span class="ident">forward</span></span>(<span>self, *args, **kwargs) ‑> Callable[..., Any]</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def forward(self, *args, **kwargs):
    if self._forward_flow is None:
        self._forward_flow = self._flow.with_outputs(self._default_outputs)
    return self._forward_flow(*args, **kwargs)</code></pre>
</details>
</dd>
</dl>
</dd>
<dt id="silk.backbones.silk.silk.SiLKLoFTR"><code class="flex name class">
<span>class <span class="ident">SiLKLoFTR</span></span>
<span>(</span><span>in_channels, *, initial_dim: int = 128, block_dims: Tuple[int] = (128, 196, 256), lat_channels: int = 256, desc_channels: int = 256, use_batchnorm: bool = True, backbone=None, detector_head=None, descriptor_head=None, detection_threshold: float = 0.8, detection_top_k: int = 100, nms_dist=4, border_dist=4, descriptor_scale_factor: float = 1.0, learnable_descriptor_scale_factor: bool = False, resolution_preserving: bool = False, padding: int = 1, **base_kwargs)</span>
</code></dt>
<dd>
<div class="desc"><p>Base class for all neural network modules.</p>
<p>Your models should also subclass this class.</p>
<p>Modules can also contain other Modules, allowing to nest them in
a tree structure. You can assign the submodules as regular attributes::</p>
<pre><code>import torch.nn as nn
import torch.nn.functional as F

class Model(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))
</code></pre>
<p>Submodules assigned in this way will be registered, and will have their
parameters converted too when you call :meth:<code>to</code>, etc.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>As per the example above, an <code>__init__()</code> call to the parent class
must be made before assignment on the child.</p>
</div>
<p>:ivar training: Boolean represents whether this module is in training or
evaluation mode.
:vartype training: bool</p>
<p>Initializes internal Module state, shared by both nn.Module and ScriptModule.</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class SiLKLoFTR(SiLKBase):
    def __init__(
        self,
        in_channels,
        *,
        initial_dim: int = 128,
        block_dims: Tuple[int] = (128, 196, 256),
        lat_channels: int = 256,
        desc_channels: int = 256,
        use_batchnorm: bool = True,
        backbone=None,
        detector_head=None,
        descriptor_head=None,
        detection_threshold: float = 0.8,
        detection_top_k: int = 100,
        nms_dist=4,
        border_dist=4,
        descriptor_scale_factor: float = 1.0,
        learnable_descriptor_scale_factor: bool = False,
        resolution_preserving: bool = False,
        padding: int = 1,
        **base_kwargs,
    ) -&gt; None:

        backbone = (
            ResNetFPN_8_2(
                {
                    &#34;in_channels&#34;: in_channels,
                    &#34;initial_dim&#34;: initial_dim,
                    &#34;block_dims&#34;: block_dims,
                    &#34;resolution_preserving&#34;: resolution_preserving,
                    &#34;padding&#34;: padding,
                }
            )
            if backbone is None
            else backbone
        )

        feat_channels = block_dims[0]

        detector_head = (
            VGGDetectorHead(
                in_channels=feat_channels,
                lat_channels=lat_channels,
                out_channels=1,
                use_batchnorm=use_batchnorm,
                padding=padding,
            )
            if detector_head is None
            else detector_head
        )

        descriptor_head = (
            VGGDescriptorHead(
                in_channels=feat_channels,
                out_channels=desc_channels,
                use_batchnorm=use_batchnorm,
                padding=padding,
            )
            if descriptor_head is None
            else descriptor_head
        )

        super().__init__(
            backbone=backbone,
            backbone_output_name=(&#34;low_res_features&#34;, &#34;features&#34;),
            **base_kwargs,
        )

        self.add_detector_head(&#34;logits&#34;, detector_head, backbone_output_name=&#34;features&#34;)
        self.add_descriptor_head(
            &#34;raw_descriptors&#34;,
            descriptor_head,
            backbone_output_name=&#34;features&#34;,
        )

        # TODO : Learneable ?
        if learnable_descriptor_scale_factor:
            self.descriptor_scale_factor = nn.parameter.Parameter(
                torch.tensor(descriptor_scale_factor)
            )
        else:
            self.descriptor_scale_factor = descriptor_scale_factor

        MagicPoint.add_detector_head_post_processing(
            self.flow,
            &#34;logits&#34;,
            prefix=&#34;&#34;,
            cell_size=1,
            detection_threshold=detection_threshold,
            detection_top_k=detection_top_k,
            nms_dist=nms_dist,
            border_dist=border_dist,
        )

        SiLKVGG.add_descriptor_head_post_processing(
            self.flow,
            input_name=self.backbone.input_name,
            descriptor_head_output_name=&#34;raw_descriptors&#34;,
            prefix=&#34;&#34;,
            scale_factor=self.descriptor_scale_factor,
        )</code></pre>
</details>
<h3>Ancestors</h3>
<ul class="hlist">
<li><a title="silk.backbones.silk.silk.SiLKBase" href="#silk.backbones.silk.silk.SiLKBase">SiLKBase</a></li>
<li><a title="silk.flow.AutoForward" href="../../flow.html#silk.flow.AutoForward">AutoForward</a></li>
<li>torch.nn.modules.module.Module</li>
</ul>
<h3>Class variables</h3>
<dl>
<dt id="silk.backbones.silk.silk.SiLKLoFTR.dump_patches"><code class="name">var <span class="ident">dump_patches</span> : bool</code></dt>
<dd>
<div class="desc"></div>
</dd>
<dt id="silk.backbones.silk.silk.SiLKLoFTR.training"><code class="name">var <span class="ident">training</span> : bool</code></dt>
<dd>
<div class="desc"></div>
</dd>
</dl>
<h3>Methods</h3>
<dl>
<dt id="silk.backbones.silk.silk.SiLKLoFTR.forward"><code class="name flex">
<span>def <span class="ident">forward</span></span>(<span>self, *args, **kwargs) ‑> Callable[..., Any]</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def forward(self, *args, **kwargs):
    if self._forward_flow is None:
        self._forward_flow = self._flow.with_outputs(self._default_outputs)
    return self._forward_flow(*args, **kwargs)</code></pre>
</details>
</dd>
</dl>
</dd>
<dt id="silk.backbones.silk.silk.SiLKVGG"><code class="flex name class">
<span>class <span class="ident">SiLKVGG</span></span>
<span>(</span><span>in_channels, *, feat_channels: int = 128, lat_channels: int = 128, desc_channels: int = 128, use_batchnorm: bool = True, backbone=None, detector_head=None, descriptor_head=None, detection_threshold: float = 0.8, detection_top_k: int = 100, nms_dist=4, border_dist=4, descriptor_scale_factor: float = 1.0, learnable_descriptor_scale_factor: bool = False, normalize_descriptors: bool = True, padding: int = 1, **base_kwargs)</span>
</code></dt>
<dd>
<div class="desc"><p>Base class for all neural network modules.</p>
<p>Your models should also subclass this class.</p>
<p>Modules can also contain other Modules, allowing to nest them in
a tree structure. You can assign the submodules as regular attributes::</p>
<pre><code>import torch.nn as nn
import torch.nn.functional as F

class Model(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))
</code></pre>
<p>Submodules assigned in this way will be registered, and will have their
parameters converted too when you call :meth:<code>to</code>, etc.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>As per the example above, an <code>__init__()</code> call to the parent class
must be made before assignment on the child.</p>
</div>
<p>:ivar training: Boolean represents whether this module is in training or
evaluation mode.
:vartype training: bool</p>
<p>Initializes internal Module state, shared by both nn.Module and ScriptModule.</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class SiLKVGG(SiLKBase):
    def __init__(
        self,
        in_channels,
        *,
        feat_channels: int = 128,
        lat_channels: int = 128,
        desc_channels: int = 128,
        use_batchnorm: bool = True,
        backbone=None,
        detector_head=None,
        descriptor_head=None,
        detection_threshold: float = 0.8,
        detection_top_k: int = 100,
        nms_dist=4,
        border_dist=4,
        descriptor_scale_factor: float = 1.0,
        learnable_descriptor_scale_factor: bool = False,
        normalize_descriptors: bool = True,
        padding: int = 1,
        **base_kwargs,
    ) -&gt; None:

        backbone = (
            VGGBackbone(
                num_channels=in_channels,
                use_batchnorm=use_batchnorm,
                use_max_pooling=False,
                padding=padding,
            )
            if backbone is None
            else backbone
        )

        detector_head = (
            VGGDetectorHead(
                in_channels=feat_channels,
                lat_channels=lat_channels,
                out_channels=1,
                use_batchnorm=use_batchnorm,
                padding=padding,
            )
            if detector_head is None
            else detector_head
        )

        descriptor_head = (
            VGGDescriptorHead(
                in_channels=feat_channels,
                out_channels=desc_channels,
                use_batchnorm=use_batchnorm,
                padding=padding,
            )
            if descriptor_head is None
            else descriptor_head
        )

        SiLKBase.__init__(
            self,
            backbone=backbone,
            **base_kwargs,
        )

        self.add_detector_head(&#34;logits&#34;, detector_head)
        self.add_descriptor_head(&#34;raw_descriptors&#34;, descriptor_head)

        self.descriptor_scale_factor = nn.parameter.Parameter(
            torch.tensor(descriptor_scale_factor),
            requires_grad=learnable_descriptor_scale_factor,
        )
        self.normalize_descriptors = normalize_descriptors

        MagicPoint.add_detector_head_post_processing(
            self.flow,
            &#34;logits&#34;,
            prefix=&#34;&#34;,
            cell_size=1,
            detection_threshold=detection_threshold,
            detection_top_k=detection_top_k,
            nms_dist=nms_dist,
            border_dist=border_dist,
        )

        SiLKVGG.add_descriptor_head_post_processing(
            self.flow,
            input_name=self.backbone.input_name,
            descriptor_head_output_name=&#34;raw_descriptors&#34;,
            prefix=&#34;&#34;,
            scale_factor=self.descriptor_scale_factor,
            normalize_descriptors=normalize_descriptors,
        )

    @staticmethod
    def add_descriptor_head_post_processing(
        flow: Flow,
        input_name: str = &#34;images&#34;,
        descriptor_head_output_name: str = &#34;raw_descriptors&#34;,
        positions_name: str = &#34;positions&#34;,
        prefix: str = &#34;superpoint.&#34;,
        scale_factor: float = 1.0,
        normalize_descriptors: bool = True,
    ):
        flow.define_transition(
            f&#34;{prefix}normalized_descriptors&#34;,
            partial(
                SuperPoint.normalize_descriptors,
                scale_factor=scale_factor,
                normalize=normalize_descriptors,
            ),
            descriptor_head_output_name,
        )
        flow.define_transition(
            f&#34;{prefix}dense_descriptors&#34;,
            SiLKVGG.get_dense_descriptors,
            f&#34;{prefix}normalized_descriptors&#34;,
        )
        flow.define_transition(f&#34;{prefix}image_size&#34;, SuperPoint.image_size, input_name)
        flow.define_transition(
            f&#34;{prefix}sparse_descriptors&#34;,
            partial(
                SiLKVGG.sparsify_descriptors,
                scale_factor=scale_factor,
                normalize_descriptors=normalize_descriptors,
            ),
            descriptor_head_output_name,
            positions_name,
        )
        flow.define_transition(
            f&#34;{prefix}sparse_positions&#34;,
            lambda x: x,
            positions_name,
        )
        flow.define_transition(
            f&#34;{prefix}dense_positions&#34;,
            SiLKVGG.get_dense_positions,
            &#34;probability&#34;,
        )

    @staticmethod
    def get_dense_positions(probability):
        batch_size = probability.shape[0]
        device = probability.device
        dense_positions = get_dense_positions(
            probability.shape[2],
            probability.shape[3],
            device,
            batch_size=batch_size,
        )

        dense_probability = probability.reshape(probability.shape[0], -1, 1)
        dense_positions = torch.cat((dense_positions, dense_probability), dim=2)

        return dense_positions

    @staticmethod
    def get_dense_descriptors(normalized_descriptors):
        dense_descriptors = normalized_descriptors.reshape(
            normalized_descriptors.shape[0],
            normalized_descriptors.shape[1],
            -1,
        )
        dense_descriptors = dense_descriptors.permute(0, 2, 1)
        return dense_descriptors

    @staticmethod
    def sparsify_descriptors(
        raw_descriptors,
        positions,
        scale_factor: float = 1.0,
        normalize_descriptors: bool = True,
    ):
        sparse_descriptors = []
        for i, pos in enumerate(positions):
            pos = pos[:, :2]
            pos = pos.floor().long()

            descriptors = raw_descriptors[i, :, pos[:, 0], pos[:, 1]].T

            # L2 normalize the descriptors
            descriptors = SuperPoint.normalize_descriptors(
                descriptors,
                scale_factor,
                normalize_descriptors,
            )

            sparse_descriptors.append(descriptors)
        return tuple(sparse_descriptors)</code></pre>
</details>
<h3>Ancestors</h3>
<ul class="hlist">
<li><a title="silk.backbones.silk.silk.SiLKBase" href="#silk.backbones.silk.silk.SiLKBase">SiLKBase</a></li>
<li><a title="silk.flow.AutoForward" href="../../flow.html#silk.flow.AutoForward">AutoForward</a></li>
<li>torch.nn.modules.module.Module</li>
</ul>
<h3>Class variables</h3>
<dl>
<dt id="silk.backbones.silk.silk.SiLKVGG.dump_patches"><code class="name">var <span class="ident">dump_patches</span> : bool</code></dt>
<dd>
<div class="desc"></div>
</dd>
<dt id="silk.backbones.silk.silk.SiLKVGG.training"><code class="name">var <span class="ident">training</span> : bool</code></dt>
<dd>
<div class="desc"></div>
</dd>
</dl>
<h3>Static methods</h3>
<dl>
<dt id="silk.backbones.silk.silk.SiLKVGG.add_descriptor_head_post_processing"><code class="name flex">
<span>def <span class="ident">add_descriptor_head_post_processing</span></span>(<span>flow: <a title="silk.flow.Flow" href="../../flow.html#silk.flow.Flow">Flow</a>, input_name: str = 'images', descriptor_head_output_name: str = 'raw_descriptors', positions_name: str = 'positions', prefix: str = 'superpoint.', scale_factor: float = 1.0, normalize_descriptors: bool = True)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">@staticmethod
def add_descriptor_head_post_processing(
    flow: Flow,
    input_name: str = &#34;images&#34;,
    descriptor_head_output_name: str = &#34;raw_descriptors&#34;,
    positions_name: str = &#34;positions&#34;,
    prefix: str = &#34;superpoint.&#34;,
    scale_factor: float = 1.0,
    normalize_descriptors: bool = True,
):
    flow.define_transition(
        f&#34;{prefix}normalized_descriptors&#34;,
        partial(
            SuperPoint.normalize_descriptors,
            scale_factor=scale_factor,
            normalize=normalize_descriptors,
        ),
        descriptor_head_output_name,
    )
    flow.define_transition(
        f&#34;{prefix}dense_descriptors&#34;,
        SiLKVGG.get_dense_descriptors,
        f&#34;{prefix}normalized_descriptors&#34;,
    )
    flow.define_transition(f&#34;{prefix}image_size&#34;, SuperPoint.image_size, input_name)
    flow.define_transition(
        f&#34;{prefix}sparse_descriptors&#34;,
        partial(
            SiLKVGG.sparsify_descriptors,
            scale_factor=scale_factor,
            normalize_descriptors=normalize_descriptors,
        ),
        descriptor_head_output_name,
        positions_name,
    )
    flow.define_transition(
        f&#34;{prefix}sparse_positions&#34;,
        lambda x: x,
        positions_name,
    )
    flow.define_transition(
        f&#34;{prefix}dense_positions&#34;,
        SiLKVGG.get_dense_positions,
        &#34;probability&#34;,
    )</code></pre>
</details>
</dd>
<dt id="silk.backbones.silk.silk.SiLKVGG.get_dense_descriptors"><code class="name flex">
<span>def <span class="ident">get_dense_descriptors</span></span>(<span>normalized_descriptors)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">@staticmethod
def get_dense_descriptors(normalized_descriptors):
    dense_descriptors = normalized_descriptors.reshape(
        normalized_descriptors.shape[0],
        normalized_descriptors.shape[1],
        -1,
    )
    dense_descriptors = dense_descriptors.permute(0, 2, 1)
    return dense_descriptors</code></pre>
</details>
</dd>
<dt id="silk.backbones.silk.silk.SiLKVGG.get_dense_positions"><code class="name flex">
<span>def <span class="ident">get_dense_positions</span></span>(<span>probability)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">@staticmethod
def get_dense_positions(probability):
    batch_size = probability.shape[0]
    device = probability.device
    dense_positions = get_dense_positions(
        probability.shape[2],
        probability.shape[3],
        device,
        batch_size=batch_size,
    )

    dense_probability = probability.reshape(probability.shape[0], -1, 1)
    dense_positions = torch.cat((dense_positions, dense_probability), dim=2)

    return dense_positions</code></pre>
</details>
</dd>
<dt id="silk.backbones.silk.silk.SiLKVGG.sparsify_descriptors"><code class="name flex">
<span>def <span class="ident">sparsify_descriptors</span></span>(<span>raw_descriptors, positions, scale_factor: float = 1.0, normalize_descriptors: bool = True)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">@staticmethod
def sparsify_descriptors(
    raw_descriptors,
    positions,
    scale_factor: float = 1.0,
    normalize_descriptors: bool = True,
):
    sparse_descriptors = []
    for i, pos in enumerate(positions):
        pos = pos[:, :2]
        pos = pos.floor().long()

        descriptors = raw_descriptors[i, :, pos[:, 0], pos[:, 1]].T

        # L2 normalize the descriptors
        descriptors = SuperPoint.normalize_descriptors(
            descriptors,
            scale_factor,
            normalize_descriptors,
        )

        sparse_descriptors.append(descriptors)
    return tuple(sparse_descriptors)</code></pre>
</details>
</dd>
</dl>
<h3>Methods</h3>
<dl>
<dt id="silk.backbones.silk.silk.SiLKVGG.forward"><code class="name flex">
<span>def <span class="ident">forward</span></span>(<span>self, *args, **kwargs) ‑> Callable[..., Any]</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def forward(self, *args, **kwargs):
    if self._forward_flow is None:
        self._forward_flow = self._flow.with_outputs(self._default_outputs)
    return self._forward_flow(*args, **kwargs)</code></pre>
</details>
</dd>
</dl>
</dd>
</dl>
</section>
</article>
<nav id="sidebar">
<h1>Index</h1>
<div class="toc">
<ul></ul>
</div>
<ul id="index">
<li><h3>Super-module</h3>
<ul>
<li><code><a title="silk.backbones.silk" href="index.html">silk.backbones.silk</a></code></li>
</ul>
</li>
<li><h3><a href="#header-functions">Functions</a></h3>
<ul class="">
<li><code><a title="silk.backbones.silk.silk.from_feature_coords_to_image_coords" href="#silk.backbones.silk.silk.from_feature_coords_to_image_coords">from_feature_coords_to_image_coords</a></code></li>
</ul>
</li>
<li><h3><a href="#header-classes">Classes</a></h3>
<ul>
<li>
<h4><code><a title="silk.backbones.silk.silk.SiLKBase" href="#silk.backbones.silk.silk.SiLKBase">SiLKBase</a></code></h4>
<ul class="">
<li><code><a title="silk.backbones.silk.silk.SiLKBase.add_descriptor_head" href="#silk.backbones.silk.silk.SiLKBase.add_descriptor_head">add_descriptor_head</a></code></li>
<li><code><a title="silk.backbones.silk.silk.SiLKBase.add_detector_head" href="#silk.backbones.silk.silk.SiLKBase.add_detector_head">add_detector_head</a></code></li>
<li><code><a title="silk.backbones.silk.silk.SiLKBase.coordinate_mapping_composer" href="#silk.backbones.silk.silk.SiLKBase.coordinate_mapping_composer">coordinate_mapping_composer</a></code></li>
<li><code><a title="silk.backbones.silk.silk.SiLKBase.dump_patches" href="#silk.backbones.silk.silk.SiLKBase.dump_patches">dump_patches</a></code></li>
<li><code><a title="silk.backbones.silk.silk.SiLKBase.forward" href="#silk.backbones.silk.silk.SiLKBase.forward">forward</a></code></li>
<li><code><a title="silk.backbones.silk.silk.SiLKBase.training" href="#silk.backbones.silk.silk.SiLKBase.training">training</a></code></li>
</ul>
</li>
<li>
<h4><code><a title="silk.backbones.silk.silk.SiLKLoFTR" href="#silk.backbones.silk.silk.SiLKLoFTR">SiLKLoFTR</a></code></h4>
<ul class="">
<li><code><a title="silk.backbones.silk.silk.SiLKLoFTR.dump_patches" href="#silk.backbones.silk.silk.SiLKLoFTR.dump_patches">dump_patches</a></code></li>
<li><code><a title="silk.backbones.silk.silk.SiLKLoFTR.forward" href="#silk.backbones.silk.silk.SiLKLoFTR.forward">forward</a></code></li>
<li><code><a title="silk.backbones.silk.silk.SiLKLoFTR.training" href="#silk.backbones.silk.silk.SiLKLoFTR.training">training</a></code></li>
</ul>
</li>
<li>
<h4><code><a title="silk.backbones.silk.silk.SiLKVGG" href="#silk.backbones.silk.silk.SiLKVGG">SiLKVGG</a></code></h4>
<ul class="">
<li><code><a title="silk.backbones.silk.silk.SiLKVGG.add_descriptor_head_post_processing" href="#silk.backbones.silk.silk.SiLKVGG.add_descriptor_head_post_processing">add_descriptor_head_post_processing</a></code></li>
<li><code><a title="silk.backbones.silk.silk.SiLKVGG.dump_patches" href="#silk.backbones.silk.silk.SiLKVGG.dump_patches">dump_patches</a></code></li>
<li><code><a title="silk.backbones.silk.silk.SiLKVGG.forward" href="#silk.backbones.silk.silk.SiLKVGG.forward">forward</a></code></li>
<li><code><a title="silk.backbones.silk.silk.SiLKVGG.get_dense_descriptors" href="#silk.backbones.silk.silk.SiLKVGG.get_dense_descriptors">get_dense_descriptors</a></code></li>
<li><code><a title="silk.backbones.silk.silk.SiLKVGG.get_dense_positions" href="#silk.backbones.silk.silk.SiLKVGG.get_dense_positions">get_dense_positions</a></code></li>
<li><code><a title="silk.backbones.silk.silk.SiLKVGG.sparsify_descriptors" href="#silk.backbones.silk.silk.SiLKVGG.sparsify_descriptors">sparsify_descriptors</a></code></li>
<li><code><a title="silk.backbones.silk.silk.SiLKVGG.training" href="#silk.backbones.silk.silk.SiLKVGG.training">training</a></code></li>
</ul>
</li>
</ul>
</li>
</ul>
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